diff --git a/R/GetProbs.R b/R/GetProbs.R
index 9960c53ff3604d6f1fff182290d647fea449df5b..25251ce9e9d1c8d2caea187341b5a73cc0a0613c 100644
--- a/R/GetProbs.R
+++ b/R/GetProbs.R
@@ -9,7 +9,8 @@
 #'each category. For observations (or forecast without member dimension), 1 
 #'means that the event happened, while 0 indicates that the event did not 
 #'happen. Weighted probabilities can be computed if the weights are provided for 
-#'each ensemble member and time step.
+#'each ensemble member and time step. The absolute thresholds can also be
+#'provided directly for probabilities calculation.
 #'
 #'@param data A named numerical array of the forecasts or observations with, at 
 #'  least, time dimension.
@@ -22,9 +23,21 @@
 #'@param prob_thresholds A numeric vector of the relative thresholds (from 0 to
 #'  1) between the categories. The default value is c(1/3, 2/3), which 
 #'  corresponds to tercile equiprobable categories.
+#'@param abs_thresholds A numeric array or vector of the absolute thresholds in 
+#'  the same units as \code{data}. If an array is provided, it should have at 
+#'  least 'bin_dim_abs' dimension. If it has more dimensions (e.g. different 
+#'  thresholds for different locations, i.e. lon and lat dimensions), they 
+#'  should match the dimensions of \code{data}, except the member dimension 
+#'  which should not be included. The default value is NULL and, in this case, 
+#'  'prob_thresholds' is used for calculating the probabilities.
+#'@param bin_dim_abs A character string of the dimension name of 
+#'  'abs_thresholds' array in which category limits are stored. It will also be
+#'  the probabilistic category dimension name in the output. The default value
+#'  is 'bin'.
 #'@param indices_for_quantiles A vector of the indices to be taken along 
 #'  'time_dim' for computing the absolute thresholds between the probabilistic
-#'  categories. If NULL, the whole period is used. The default value is NULL.
+#'  categories. If NULL (default), the whole period is used. It is only used 
+#'  when 'prob_thresholds' is provided.
 #'@param weights A named numerical array of the weights for 'data' with 
 #'  dimensions 'time_dim' and 'memb_dim' (if 'data' has them). The default value
 #'  is NULL. The ensemble should have at least 70 members or span at least 10 
@@ -37,20 +50,30 @@
 #'  computation. The default value is NULL.
 #'
 #'@return 
-#'A numerical array of probabilities with dimensions c(bin, the rest dimensions
-#'of 'data' except 'memb_dim'). 'bin' dimension has the length of probabilistic 
-#'categories, i.e., \code{length(prob_thresholds) + 1}.
+#'A numerical array of probabilities with dimensions c(bin_dim_abs, the rest
+#'dimensions of 'data' except 'memb_dim'). 'bin' dimension has the length of 
+#'probabilistic categories, i.e., \code{length(prob_thresholds) + 1}.
 #'
 #'@examples
 #'data <- array(rnorm(2000), dim = c(ensemble = 25, sdate = 20, time = 4))
 #'res <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble', 
 #'                indices_for_quantiles = 4:17)
 #'
+#'# abs_thresholds is provided
+#'abs_thr1 <- c(-0.2, 0.3)
+#'abs_thr2 <- array(c(-0.2, 0.3) + rnorm(40) * 0.1, dim = c(cat = 2, sdate = 20))
+#'res1 <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble',
+#'                 prob_thresholds = NULL, abs_thresholds = abs_thr1)
+#'res2 <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble',
+#'                 prob_thresholds = NULL, abs_thresholds = abs_thr2, bin_dim_abs = 'cat')
+#'
 #'@import multiApply
 #'@importFrom easyVerification convert2prob
 #'@export
-GetProbs <- function(data, time_dim = 'sdate', memb_dim = 'member', indices_for_quantiles = NULL, 
-                     prob_thresholds = c(1/3, 2/3), weights = NULL, cross.val = FALSE, ncores = NULL) {
+GetProbs <- function(data, time_dim = 'sdate', memb_dim = 'member',
+                     indices_for_quantiles = NULL, 
+                     prob_thresholds = c(1/3, 2/3), abs_thresholds = NULL,
+                     bin_dim_abs = 'bin', weights = NULL, cross.val = FALSE, ncores = NULL) {
 
   # Check inputs
   ## data
@@ -79,23 +102,67 @@ GetProbs <- function(data, time_dim = 'sdate', memb_dim = 'member', indices_for_
            "dimension exists, set it as NULL.")
     }
   }
-  ## prob_thresholds
-  if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) |
-      any(prob_thresholds <= 0) | any(prob_thresholds >= 1)) {
-    stop("Parameter 'prob_thresholds' must be a numeric vector between 0 and 1.")
+  ## bin_dim_abs
+  if (!is.character(bin_dim_abs) | length(bin_dim_abs) != 1) {
+    stop('Parameter "bin_dim_abs" must be a character string.')
   }
-  ## indices_for_quantiles
-  if (is.null(indices_for_quantiles)) {
-    indices_for_quantiles <- 1:dim(data)[time_dim]
-  } else {
-    if (!is.numeric(indices_for_quantiles) | !is.vector(indices_for_quantiles)) {
-      stop("Parameter 'indices_for_quantiles' must be NULL or a numeric vector.")
-    } else if (length(indices_for_quantiles) > dim(data)[time_dim] |
-               max(indices_for_quantiles) > dim(data)[time_dim] |
-               any(indices_for_quantiles < 1)) {
-      stop("Parameter 'indices_for_quantiles' should be the indices of 'time_dim'.")
+  ## prob_thresholds, abs_thresholds
+  if (!is.null(abs_thresholds) & !is.null(prob_thresholds)) {
+    .warning(paste0("Parameters 'prob_thresholds' and 'abs_thresholds' are both provided. ",
+                    "Only the first one is used."))
+    abs_thresholds <- NULL
+  } else if (is.null(abs_thresholds) & is.null(prob_thresholds)) {
+    stop("One of the parameters 'prob_thresholds' and 'abs_thresholds' must be provided.")
+  }
+  if (!is.null(prob_thresholds)) {
+    if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) |
+        any(prob_thresholds <= 0) | any(prob_thresholds >= 1)) {
+      stop("Parameter 'prob_thresholds' must be a numeric vector between 0 and 1.")
+    }
+    ## indices_for_quantiles
+    if (is.null(indices_for_quantiles)) {
+      indices_for_quantiles <- 1:dim(data)[time_dim]
+    } else {
+      if (!is.numeric(indices_for_quantiles) | !is.vector(indices_for_quantiles)) {
+        stop("Parameter 'indices_for_quantiles' must be NULL or a numeric vector.")
+      } else if (length(indices_for_quantiles) > dim(data)[time_dim] |
+                 max(indices_for_quantiles) > dim(data)[time_dim] |
+                 any(indices_for_quantiles < 1)) {
+        stop("Parameter 'indices_for_quantiles' should be the indices of 'time_dim'.")
+      }
+    }
+
+  } else {  # abs_thresholds
+
+    if (is.null(dim(abs_thresholds))) {  # a vector
+      dim(abs_thresholds) <- length(abs_thresholds)
+      names(dim(abs_thresholds)) <- bin_dim_abs
+    }
+    # bin_dim_abs
+    if (!(bin_dim_abs %in% names(dim(abs_thresholds)))) {
+      stop("Parameter abs_thresholds' can be a vector or array with 'bin_dim_abs' dimension.")
+    }
+    if (!is.null(memb_dim) && memb_dim %in% names(dim(abs_thresholds))) {
+      stop("Parameter abs_thresholds' cannot have member dimension.")
+    }
+    dim_name_abs <- names(dim(abs_thresholds))[which(names(dim(abs_thresholds)) != bin_dim_abs)]
+    if (any(!dim_name_abs %in% names(dim(data)))) {
+      stop("Parameter 'abs_thresholds' dimensions except 'bin_dim_abs' must be in 'data' as well.")
+    } else {
+      if (any(dim(abs_thresholds)[dim_name_abs] != dim(data)[dim_name_abs])) {
+        stop("Parameter 'abs_thresholds' dimensions must have the same length as 'data'.")
+      }
     }
+    if (!is.null(indices_for_quantiles)) {
+      warning("Parameter 'indices_for_quantiles' is not used when 'abs_thresholds' are provided.")
+    }
+    abs_target_dims <- bin_dim_abs
+    if (time_dim %in% names(dim(abs_thresholds))) {
+      abs_target_dims <- c(bin_dim_abs, time_dim)
+    }
+
   }
+
   ## weights
   if (!is.null(weights)) {
     if (!is.array(weights) | !is.numeric(weights))
@@ -145,62 +212,83 @@ GetProbs <- function(data, time_dim = 'sdate', memb_dim = 'member', indices_for_
   }
 
   ###############################
-
-  res <- Apply(data = list(data = data), 
-               target_dims = c(time_dim, memb_dim), #, dat_dim), 
-               output_dims = c("bin", time_dim),
-               fun = .GetProbs,
-#               dat_dim = dat_dim, 
-               prob_thresholds = prob_thresholds, 
-               indices_for_quantiles = indices_for_quantiles, 
-               weights = weights, cross.val = cross.val, ncores = ncores)$output1
+  if (is.null(abs_thresholds)) {
+    res <- Apply(data = list(data = data), 
+                 target_dims = c(time_dim, memb_dim), 
+                 output_dims = c(bin_dim_abs, time_dim),
+                 fun = .GetProbs,
+                 prob_thresholds = prob_thresholds,
+                 indices_for_quantiles = indices_for_quantiles, 
+                 weights = weights, cross.val = cross.val, ncores = ncores)$output1
+  } else {
+    res <- Apply(data = list(data = data, abs_thresholds = abs_thresholds),
+                 target_dims = list(c(time_dim, memb_dim), abs_target_dims),
+                 output_dims = c(bin_dim_abs, time_dim),
+                 fun = .GetProbs,
+                 prob_thresholds = NULL,
+                 indices_for_quantiles = NULL,
+                 weights = NULL, cross.val = FALSE, ncores = ncores)$output1
+  }                 
  
   return(res)
 }
 
 .GetProbs <- function(data, indices_for_quantiles,
-                      prob_thresholds = c(1/3, 2/3), weights = NULL, cross.val = FALSE) {
+                      prob_thresholds = c(1/3, 2/3), abs_thresholds = NULL, 
+                      weights = NULL, cross.val = FALSE) {
   # .GetProbs() is used in RPS, RPSS, ROCSS
   # data
   ## if data is exp: [sdate, memb]
   ## if data is obs: [sdate, (memb)]
   # weights: [sdate, (memb)], same as data
+  # if abs_thresholds is not NULL: [bin, (sdate)]
 
   # Add dim [memb = 1] to data if it doesn't have memb_dim
   if (length(dim(data)) == 1) {
     dim(data) <- c(dim(data), 1)
     if (!is.null(weights)) dim(weights) <- c(dim(weights), 1)
   }
-  # Absolute thresholds
-  if (cross.val) {
-    quantiles <- array(NA, dim = c(bin = length(prob_thresholds), sdate = dim(data)[1]))
-    for (i_time in 1:dim(data)[1]) {
+
+  # Calculate absolute thresholds
+  if (is.null(abs_thresholds)) {
+    if (cross.val) {
+      quantiles <- array(NA, dim = c(bin = length(prob_thresholds), sdate = dim(data)[1]))
+      for (i_time in 1:dim(data)[1]) {
+        if (is.null(weights)) {
+          quantiles[, i_time] <- quantile(x = as.vector(data[indices_for_quantiles[which(indices_for_quantiles != i_time)], ]),
+                                          probs = prob_thresholds, type = 8, na.rm = TRUE)
+        } else {
+          # weights: [sdate, memb]
+          sorted_arrays <- .sorted_distributions(data[indices_for_quantiles[which(indices_for_quantiles != i_time)], ], 
+                                                 weights[indices_for_quantiles[which(indices_for_quantiles != i_time)], ])
+          sorted_data <- sorted_arrays$data
+          cumulative_weights <- sorted_arrays$cumulative_weights
+          quantiles[, i_time] <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y
+        }
+      }
+
+    } else {
       if (is.null(weights)) {
-        quantiles[, i_time] <- quantile(x = as.vector(data[indices_for_quantiles[which(indices_for_quantiles != i_time)], ]),
-                                        probs = prob_thresholds, type = 8, na.rm = TRUE)
+        quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), 
+                              probs = prob_thresholds, type = 8, na.rm = TRUE)
       } else {
         # weights: [sdate, memb]
-        sorted_arrays <- .sorted_distributions(data[indices_for_quantiles[which(indices_for_quantiles != i_time)], ], 
-                                               weights[indices_for_quantiles[which(indices_for_quantiles != i_time)], ])
+        sorted_arrays <- .sorted_distributions(data[indices_for_quantiles, ], 
+                                               weights[indices_for_quantiles, ])
         sorted_data <- sorted_arrays$data
         cumulative_weights <- sorted_arrays$cumulative_weights
-        quantiles[, i_time] <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y
+        quantiles <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y
       }
+      quantiles <- array(rep(quantiles, dim(data)[1]),
+                         dim = c(bin = length(quantiles), dim(data)[1]))
     }
 
-  } else {
-    if (is.null(weights)) {
-      quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), 
-                            probs = prob_thresholds, type = 8, na.rm = TRUE)
-    } else {
-      # weights: [sdate, memb]
-      sorted_arrays <- .sorted_distributions(data[indices_for_quantiles, ], 
-                                             weights[indices_for_quantiles, ])
-      sorted_data <- sorted_arrays$data
-      cumulative_weights <- sorted_arrays$cumulative_weights
-      quantiles <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y
+  } else { # abs_thresholds provided
+    quantiles <- abs_thresholds
+    if (length(dim(quantiles)) == 1) {
+      quantiles <- InsertDim(quantiles, len = dim(data)[1],
+                             pos = 2, name = names(dim(data))[1])
     }
-    quantiles <- array(rep(quantiles, dim(data)[1]), dim = c(bin = length(quantiles), dim(data)[1]))
   }
   # quantiles: [bin-1, sdate]
 
diff --git a/R/MSE.R b/R/MSE.R
index 97e4e828e88f85fea639fb63520b54da7366db6b..6b67083a837f3d646a089955dc2591908471ad95 100644
--- a/R/MSE.R
+++ b/R/MSE.R
@@ -69,7 +69,7 @@
 #'  
 #'exp2 <- array(rnorm(20), dim = c(sdate = 5, member = 4))
 #'obs2 <- array(rnorm(10),  dim = c(sdate = 5, member = 2))
-#'res2 <- MSE(exp3, obs3, memb_dim = 'member')
+#'res2 <- MSE(exp2, obs2, memb_dim = 'member')
 #'
 #'@import multiApply
 #'@importFrom ClimProjDiags Subset
diff --git a/man/GetProbs.Rd b/man/GetProbs.Rd
index fd84d2f878ecb208baafdf9176e67dfc145070d5..06ad046a1764d59c76f61028281a864e116f2aad 100644
--- a/man/GetProbs.Rd
+++ b/man/GetProbs.Rd
@@ -10,6 +10,8 @@ GetProbs(
   memb_dim = "member",
   indices_for_quantiles = NULL,
   prob_thresholds = c(1/3, 2/3),
+  abs_thresholds = NULL,
+  bin_dim_abs = "bin",
   weights = NULL,
   cross.val = FALSE,
   ncores = NULL
@@ -29,12 +31,26 @@ member). The default value is 'member'.}
 
 \item{indices_for_quantiles}{A vector of the indices to be taken along 
 'time_dim' for computing the absolute thresholds between the probabilistic
-categories. If NULL, the whole period is used. The default value is NULL.}
+categories. If NULL (default), the whole period is used. It is only used 
+when 'prob_thresholds' is provided.}
 
 \item{prob_thresholds}{A numeric vector of the relative thresholds (from 0 to
 1) between the categories. The default value is c(1/3, 2/3), which 
 corresponds to tercile equiprobable categories.}
 
+\item{abs_thresholds}{A numeric array or vector of the absolute thresholds in 
+the same units as \code{data}. If an array is provided, it should have at 
+least 'bin_dim_abs' dimension. If it has more dimensions (e.g. different 
+thresholds for different locations, i.e. lon and lat dimensions), they 
+should match the dimensions of \code{data}, except the member dimension 
+which should not be included. The default value is NULL and, in this case, 
+'prob_thresholds' is used for calculating the probabilities.}
+
+\item{bin_dim_abs}{A character string of the dimension name of 
+'abs_thresholds' array in which category limits are stored. It will also be
+the probabilistic category dimension name in the output. The default value
+is 'bin'.}
+
 \item{weights}{A named numerical array of the weights for 'data' with 
 dimensions 'time_dim' and 'memb_dim' (if 'data' has them). The default value
 is NULL. The ensemble should have at least 70 members or span at least 10 
@@ -49,9 +65,9 @@ is FALSE.}
 computation. The default value is NULL.}
 }
 \value{
-A numerical array of probabilities with dimensions c(bin, the rest dimensions
-of 'data' except 'memb_dim'). 'bin' dimension has the length of probabilistic 
-categories, i.e., \code{length(prob_thresholds) + 1}.
+A numerical array of probabilities with dimensions c(bin_dim_abs, the rest
+dimensions of 'data' except 'memb_dim'). 'bin' dimension has the length of 
+probabilistic categories, i.e., \code{length(prob_thresholds) + 1}.
 }
 \description{
 Compute probabilistic forecasts from an ensemble based on the relative 
@@ -63,11 +79,20 @@ the probabilities are calculated as the percentage of members that fall into
 each category. For observations (or forecast without member dimension), 1 
 means that the event happened, while 0 indicates that the event did not 
 happen. Weighted probabilities can be computed if the weights are provided for 
-each ensemble member and time step.
+each ensemble member and time step. The absolute thresholds can also be
+provided directly for probabilities calculation.
 }
 \examples{
 data <- array(rnorm(2000), dim = c(ensemble = 25, sdate = 20, time = 4))
 res <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble', 
                indices_for_quantiles = 4:17)
 
+# abs_thresholds is provided
+abs_thr1 <- c(-0.2, 0.3)
+abs_thr2 <- array(c(-0.2, 0.3) + rnorm(40) * 0.1, dim = c(cat = 2, sdate = 20))
+res1 <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble',
+                prob_thresholds = NULL, abs_thresholds = abs_thr1)
+res2 <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble',
+                prob_thresholds = NULL, abs_thresholds = abs_thr2, bin_dim_abs = 'cat')
+
 }
diff --git a/man/MSE.Rd b/man/MSE.Rd
index 291d08c2891d54fb76c7ba7b5d083c34ad21c620..9464be6518a0065bbf4d262cb8025195268e01cb 100644
--- a/man/MSE.Rd
+++ b/man/MSE.Rd
@@ -97,6 +97,6 @@ res1 <- MSE(exp1, obs1, comp_dim = 'ftime', dat_dim = 'dat')
  
 exp2 <- array(rnorm(20), dim = c(sdate = 5, member = 4))
 obs2 <- array(rnorm(10),  dim = c(sdate = 5, member = 2))
-res2 <- MSE(exp3, obs3, memb_dim = 'member')
+res2 <- MSE(exp2, obs2, memb_dim = 'member')
 
 }
diff --git a/tests/testthat/test-GetProbs.R b/tests/testthat/test-GetProbs.R
index f1958dc22c21bf35c227df8a5159040147bdf8db..47413c1111507ba263d6e3ce1625f378938196d9 100644
--- a/tests/testthat/test-GetProbs.R
+++ b/tests/testthat/test-GetProbs.R
@@ -6,11 +6,22 @@ data1 <- array(rnorm(60), dim = c(member = 3, sdate = 10, time = 2))
 set.seed(2)
 weights1 <- array(abs(rnorm(30)), dim = c(member = 3, sdate = 10))
 
+abs_thr1_1 <- c(-0.2, 0.4)
+abs_thr1_2 <- array(abs_thr1_1, dim = c(bin = 2))
+set.seed(4)
+abs_thr1_3 <- array(abs_thr1_1 + rnorm(20)*0.1, dim = c(bin = 2, sdate = 10))
+abs_thr1_4 <- array(abs_thr1_3, dim = c(dim(abs_thr1_3), time = 2))
+
 # dat2
 set.seed(1)
 data2 <- array(rnorm(20), dim = c(sdate = 10, time = 2))
 set.seed(2)
 weights2 <- array(abs(rnorm(10)), dim = c(sdate = 10))
+abs_thr2_1 <- c(-0.2, 0.4)
+abs_thr2_2 <- array(abs_thr2_1, dim = c(bin = 2))
+set.seed(4)
+abs_thr2_3 <- array(abs_thr2_1 + rnorm(20)*0.3, dim = c(bin = 2, sdate = 10))
+abs_thr2_4 <- array(abs_thr2_3, dim = c(dim(abs_thr2_3), time = 2))
 
 ##############################################
 
@@ -47,6 +58,23 @@ test_that("1. Input checks", {
   GetProbs(data1, prob_thresholds = 1),
   "Parameter 'prob_thresholds' must be a numeric vector between 0 and 1."
   )
+  # abs_thresholds
+  expect_error(
+  GetProbs(data1, prob_thresholds = NULL, abs_thresholds = abs_thr1_2, bin_dim_abs = 'cat'),
+  "Parameter abs_thresholds' can be a vector or array with 'bin_dim_abs' dimension."
+  )
+  expect_error(
+  GetProbs(data1, prob_thresholds = NULL, abs_thresholds = array(abs_thr1_3, dim = c(dim(abs_thr1_3), member = 3))),
+  "Parameter abs_thresholds' cannot have member dimension."
+  )
+  expect_error(
+  GetProbs(data1, prob_thresholds = NULL, abs_thresholds = array(abs_thr1_3, dim = c(dim(abs_thr1_3), extra = 3))),
+  "Parameter 'abs_thresholds' dimensions except 'bin_dim_abs' must be in 'data' as well."
+  )
+  expect_error(
+  GetProbs(data1, prob_thresholds = NULL, abs_thresholds = array(abs_thr1_3, dim = c(bin = 2, sdate = 5, time = 2))),
+  "Parameter 'abs_thresholds' dimensions must have the same length as 'data'."
+  )
   # indices_for_clim
   expect_error(
   GetProbs(data1, indices_for_quantiles = array(1:6, dim = c(2, 3))),
@@ -94,6 +122,10 @@ dim(GetProbs(data1)),
 c(bin = 3, sdate = 10, time = 2)
 )
 expect_equal(
+dim(GetProbs(data1, bin_dim_abs = "cat")),
+c(cat = 3, sdate = 10, time = 2)
+)
+expect_equal(
 c(GetProbs(data1)[, 10, 2]),
 c(0.3333333, 0.3333333, 0.3333333),
 tolerance = 0.0001
@@ -173,6 +205,30 @@ c(0.3335612, 0.5277459, 0.1386929),
 tolerance = 0.0001
 )
 
+# abs_threshold
+expect_equal(
+dim(GetProbs(data1, abs_thresholds = abs_thr1_1, prob_thresholds = NULL, indices_for_quantiles = NULL)),
+c(bin = 3, sdate = 10, time = 2)
+)
+expect_equal(
+c(GetProbs(data1, abs_thresholds = abs_thr1_1, prob_thresholds = NULL, indices_for_quantiles = NULL)[, 3, 2]),
+c(0.3333333, 0.3333333, 0.3333333),
+tolerance = 0.0001
+)
+expect_equal(
+GetProbs(data1, abs_thresholds = abs_thr1_1, prob_thresholds = NULL, indices_for_quantiles = NULL),
+GetProbs(data1, abs_thresholds = abs_thr1_2, prob_thresholds = NULL, indices_for_quantiles = NULL)
+)
+expect_equal(
+c(GetProbs(data1, abs_thresholds = abs_thr1_3, prob_thresholds = NULL, indices_for_quantiles = NULL)[, 3, 2]),
+c(0.6666667, 0, 0.3333333),
+tolerance = 0.0001
+)
+expect_equal(
+GetProbs(data1, abs_thresholds = abs_thr1_3, prob_thresholds = NULL, indices_for_quantiles = NULL),
+GetProbs(data1, abs_thresholds = abs_thr1_4, prob_thresholds = NULL, indices_for_quantiles = NULL)
+)
+
 })
 
 
@@ -254,4 +310,28 @@ c(GetProbs(data2, memb_dim = NULL, cross.val = T, weights = weights2)[, 10, 2]),
 c(0, 1, 0)
 )
 
+# abs_threshold
+expect_equal(
+dim(GetProbs(data2, memb_dim = NULL, abs_thresholds = abs_thr2_1, prob_thresholds = NULL, indices_for_quantiles = NULL)),
+c(bin = 3, sdate = 10, time = 2)
+)
+expect_equal(
+c(GetProbs(data2, memb_dim = NULL, abs_thresholds = abs_thr2_1, prob_thresholds = NULL, indices_for_quantiles = NULL)[, 5, 1]),
+c(0, 1, 0),
+tolerance = 0.0001
+)
+expect_equal(
+GetProbs(data2, memb_dim = NULL, abs_thresholds = abs_thr2_1, prob_thresholds = NULL, indices_for_quantiles = NULL),
+GetProbs(data2, memb_dim = NULL, abs_thresholds = abs_thr2_2, prob_thresholds = NULL, indices_for_quantiles = NULL)
+)
+expect_equal(
+c(GetProbs(data2, memb_dim = NULL, abs_thresholds = abs_thr2_3, prob_thresholds = NULL, indices_for_quantiles = NULL)[, 5, 1]),
+c(1, 0, 0),
+tolerance = 0.0001
+)
+expect_equal(
+GetProbs(data2, memb_dim = NULL, abs_thresholds = abs_thr2_3, prob_thresholds = NULL, indices_for_quantiles = NULL),
+GetProbs(data2, memb_dim = NULL, abs_thresholds = abs_thr2_4, prob_thresholds = NULL, indices_for_quantiles = NULL)
+)
+
 })